A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

Yang, Xiaochun; Chen, Daichao; Sun, Qiushi; Wang, Yao; Xia, Yu; Yang, Jinyu; Lin, Chang; Dang, Xin; Cen, Zimu; Liang, Dongdong; Wei, Rong; Xu, Ze; Xi, Guangyin; Xue, Gang; Ye, Can; Wang, Li-Peng; Zou, Peng; Wang, Shi-Qiang; Rivera-Fuentes, Pablo; Püntener, Salome

doi:10.1038/s41421-023-00543-1

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Yang, X., Chen, D., Sun, Q., Wang, Y., Xia, Y., Yang, J., Lin, C., Dang, X., Cen, Z., Liang, D., Wei, R., Xu, Z., Xi, G., Xue, G., Ye, C., Wang, L.-P., Zou, P., Wang, S.-Q., Rivera-Fuentes, P., … Zhao, Y. (2023). A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems. Cell Discovery, 9(1), 53. https://doi.org/10.1038/s41421-023-00543-1

Abstract

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are ap